A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Graphs predict reaction conditions for organic chemistry.
METRO predicts reactions using minimal templates, reducing computational overhead and achieving state-of-the-art results.
MEGAN models chemical reactions as graph edits, improving synthesis planning.
Novel deep learning method predicts reaction coordinates and future MD trajectories.
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural netwo…
Hopfield networks improve reaction template prediction for few/zero-shot scenarios.
Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using `arrow-pushing' diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction …
The growing ubiquity of Social Media data offers an attractive perspective for improving the quality of machine learning-based models in several fields, ranging from Computer Vision to Natural Language Processing. In this paper we focus on Facebook posts paired with reactions of multiple users, and we investigate their…
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry. Since a reaction may involve hundreds of atoms, fully exploring the space of possible transformations is intractable. The current solution utilizes reaction templates to limit the space, but it suffers from coverage and eff…
Improved chemical reaction prediction using augmented NLP models.
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in…
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules.…
ChatGPT predicts stock market reactions from news headlines without financial training.
There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Consequently, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction pred…
Adjoint SA speeds up bioprocess parameter learning.
Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existing approaches rely o…
Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript. Methods: Novel kernels over drug combinations of arbitrary orders are developed within support vector machines for the prediction. Graph matching methods are …
This paper speeds up simulations of hypersonic reentry by combining traditional and neural methods.
We use the formalism of Geometrothermodynamics to describe chemical reactions in the context of equilibrium thermodynamics. Any chemical reaction in a closed system is shown to be described by a geodesic in a dimensional manifold that can be interpreted as the equilibrium space of the reaction. We first show this i…
CRNN discovers chemical reaction pathways from data.
Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels). One motivation for our study stems fr…
Upper bound on CRN reaction rates derived using information geometry.
Chemical networks outperform spiking neural networks in classification tasks.
Bayesian inference for biochemical reaction networks using jump-diffusion approximations.
We generalize the reaction-diffusion model A + B -> 0 in order to study the impact of an excess of A (or B) at the reaction front. We provide an exact solution of the model, which shows that linear response breaks down: the average displacement of the reaction front grows as the square-root of the imbalance. We argue t…
New method uses machine learning to analyze catalyst reactions.
Considered an important macroeconomic indicator, the Purchasing Managers' Index (PMI) on Manufacturing generally assumes that PMI announcements will produce an impact on stock markets. International experience suggests that stock markets react to negative PMI news. In this research, we empirically investigate the stock…
The increase in the number of Internet users and the strong interaction brought by Web 2.0 made the Opinion Mining an important task in the area of natural language processing. Although several methods are capable of performing this task, few use multi-label classification, where there is a group of true labels for eac…
Language models predict inorganic synthesis conditions and temperatures.
Study the singular limit of a boundary reaction equation, showing energy concentration and varifold support.
Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate molecules with desirable properties, they give no guarantees that the molecules can actually be synthesized in practice. We propose a new molecu…
The study distills news sources to analyze stock reactions, finding sentiment has asymmetric and sector-specific effects.
Combustion reaction kinetics models are used for the description of a special class of bursty Financial Time Series. The small number of parameters they depend upon enable financial analysts to predict the time as well as the magnitude of the jump of the value of the portfolio. Several Financial Time Series are analyse…
Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectrospatial features of the pre-task, resting-state electroencephalograms (EEG). We asked …
Paper presents a multi-label topic model for financial texts with high performance and insights into market reactions.
Reactmine infers chemical reactions from time series data, overcoming sparse model limitations.
Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can be modeled as a retrosynthesis planning problem with a catalyst allowed. This is a crucial step in efficient biomass conversion. The tradit…
Global solutions and smoothing effects for reaction-diffusion equations on manifolds.
G2Gs transforms target molecules into reactants without templates, improving accuracy.
Graph-based framework predicts ADR signals from clinical data.
Method predicts multistable system states from sparse measurements.
Study shows neural operators can efficiently solve complex reaction-diffusion systems.
Bayesian algorithm discovers synthetic routes from target molecules.
Study shows SEC crypto classification led to significant market reactions.
DeepSKA provides interpretable, reliable neural approximations for SRNs.
Study evaluates uncertainty quantification for atomistic neural networks, revealing complex relationships between error and uncertainty.
Bounds on chemical reaction network relaxation rates using convex analysis.
We address the problem of optimal Central Bank intervention in the exchange rate market when interventions create feedback in the rate dynamics. In particular, we extend the work done on optimal impulse control by Cadenillas and Zapatero to incorporate temporary market reactions, of random duration and level, to Bank i…